Liver Disease Activity Estimation with Ultrasound Medical Imaging

ABSTRACT

Ultrasound-based estimation of disease activity, such as for NAS or other activity index for NAFLD for liver disease, is provided. Ultrasound measures acoustic scatter and shear wave propagation parameters, such as measuring acoustic backscatter coefficient, shear wave velocity, and shear wave damping ratio. A score for the disease activity is determined from these scatter and shear wave propagation parameters. The physician may be assisted by relatively inexpensive and rapid ultrasound as compared to biopsy or MRI based scoring in scoring activity of a disease, such as NAFLD. Ultrasound imaging is more readily available and less expensive and MRI, and is non-invasive.

RELATED APPLICATIONS

The present patent document is a continuation-in-part of U.S. patentapplication Ser. No. 15/716,444, filed Sep. 26, 2017, which claims thebenefit of the filing date under 35 U.S.C. § 119(e) of Provisional U.S.Patent Application Ser. No. 62/482,606, filed Apr. 6, 2017. Bothapplications are hereby incorporated by reference.

BACKGROUND

The present embodiments relate to ultrasound imaging. A disease-relatedactivity in tissue, such as liver, is measured using ultrasound.

Nonalcoholic fatty liver disease (NAFLD) is the most common liverdisease in American adults and children. NAFLD is characterized byexcess hepatic fat accumulation as well as hepatic fibrosis. Fatfraction may be measured as an indicator of NAFLD. Fat fraction in theliver or other tissues, such as breast tissue, and/or other tissueproperties (e.g., degree of fibrosis) provide diagnostically usefulinformation.

Over 25% of patients with NAFLD develop non-alcoholic steatohepatitis(NASH). NASH may progress to cirrhosis and hepatocellular carcinoma. ANAFLD activity score (NAS) is used to diagnose and monitor changes orlevel of NASH. NAS is provided from histologic evaluation of liverbiopsies and is calculated as an unweighted sum of the observedsteatosis, lobular inflammation, and ballooning scores.

Magnetic resonance imaging (MRI) may measure the proton density fatfraction (PDFF) as a biomarker of hepatic fat content. MRI may be usedto further estimate NAS. However, MRI is not widely available and isexpensive.

SUMMARY

By way of introduction, the preferred embodiments described belowinclude methods, instructions, and systems for ultrasound-basedestimation of disease activity, such as for NAS or other activity indexfor NAFLD. Ultrasound measures acoustic scatter and shear wavepropagation parameters, such as measuring acoustic backscattercoefficient, shear wave velocity, and shear wave damping ratio. A scorefor the disease activity is determined from these scatter and shear wavepropagation parameters. The physician may be assisted by relativelyinexpensive and rapid ultrasound as compared to biopsy or MRI basedscoring in scoring activity of a disease, such as NAFLD. Ultrasound isnon-invasive, and more readily available and less expensive than MRI.

In a first aspect, a method is provided for non-alcoholic liver diseaseactivity estimation with an ultrasound scanner. A first measure ofscattering in tissue is generated from a scan of a patient by theultrasound scanner. The first measure of scattering is a backscattercoefficient. Second and third measures of shear wave propagation in thetissue are generated from the scan of the patient by the ultrasoundscanner. The second measure is a shear wave velocity, and the thirdmeasure is a shear wave damping ratio. A first value for anultrasound-derived liver disease activity index is estimated from thebackscatter coefficient, the shear wave velocity, and the shear wavedamping ratio. An ultrasound image including an indication of the firstvalue of the ultrasound-derived liver disease activity index asestimated is output.

In a second aspect, a system is provided for estimation of diseaseactivity. A beamformer is configured to transmit and receive sequencesof pulses in a patient with the transducer. The sequence of pulses isfor a scatter parameter and for first and second shear wave parameters.An image processor is configured to generate a score for an index of thedisease activity from a combination of the scatter parameter, the firstshear wave parameter, and the second shear wave parameter. A display isconfigured to display the score for the index of the disease activity.

In a third aspect, a method is provided for liver disease activityestimation with an ultrasound system. The ultrasound system determines aplurality of scattering parameters of liver tissue of a patient. Theultrasound system determines a plurality of shear wave parameters of theliver tissue of the patient. A fat fraction is estimated from at leastone of the scattering parameters. A level of the liver disease activityis estimated from the fat fraction and at least one of the shear waveparameters. The level of the liver disease is displayed.

The present invention is defined by the following claims, and nothing inthis section should be taken as a limitation on those claims. Furtheraspects and advantages of the invention are discussed below inconjunction with the preferred embodiments and may be later claimedindependently or in combination.

BRIEF DESCRIPTION OF THE DRAWINGS

The components and the figures are not necessarily to scale, emphasisinstead being placed upon illustrating the principles of the invention.Moreover, in the figures, like reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a flow chart diagram of one embodiment of a method forestimating a tissue property with ultrasound;

FIG. 2 is a block diagram of one embodiment of a system for estimating atissue property with ultrasound;

FIG. 3 is a flow chart diagram of one embodiment of a method forestimating disease activity, such as NAS, with ultrasound; and

FIG. 4 is an example plot showing accuracy in predicting NAS fromultrasound measures as compared to histologic NAS.

DETAILED DESCRIPTION OF THE DRAWINGS AND PRESENTLY PREFERRED EMBODIMENTS

Disease activity is estimated to assist with diagnosis, screening,monitoring, and/or predicting health conditions. For example, NAS orother liver disease activity is estimated. A score of an index isestimated using ultrasound, allowing for rapid, inexpensive, andnon-invasive estimation of the disease activity. An ultrasound-derivedNAFLD activity score is estimated, avoiding biopsy or MRI.

The disease activity may be estimated from measurements used inestimating tissue properties, such as liver fat fraction and/orestimates of the liver fat fraction. The measurements and estimation offat fraction or other tissue properties is discussed below with respectto FIG. 1. The use of these measurements and/or tissue properties inestimating the disease activity is then discussed with respect to FIG.3.

Regarding tissue property estimation, quantitative ultrasound (QUS) isused for screening, diagnosing, monitoring, and/or predicting healthconditions. The complexity of human tissue may be measured usingmultiple QUS parameters for accurate characterization of that tissue.For example, liver fat fraction is estimated using a multi-parametricapproach that combines quantitative parameters extracted from thereceived signals of different wave phenomena, such as scattering andattenuation of longitudinal waves, propagation and attenuation of shearwaves, and/or propagation and attenuation of on-axis waves from acousticradiation force impulse (ARFI).

In one embodiment, a tissue property (e.g., liver fat fraction) isestimated by transmitting and receiving a sequence of pulses to estimatescattering parameters, and by transmitting and receiving a sequence ofpulses to obtain shear wave parameters. The estimation may also includetransmitting and receiving a sequence of pulses to estimate parametersfrom axial displacements caused by acoustic radiation force impulses(ARFI). The QUS parameters are estimated and combined to estimate thetissue property. Other information may be included in the estimation ofthe tissue property, such as non-ultrasound data (e.g., bloodbiomarker).

FIG. 1 shows a method for tissue property estimation with an ultrasoundscanner or system. Tissue reactions to different types of waves or wavephenomena are measured. The combination of the measures of thesedifferent reactions is used to estimate the tissue property.

The method is implemented by the system of FIG. 2 or a different system.A medical diagnostic ultrasound scanner performs the measurements byacoustically generating the waves and measuring the responses. An imageprocessor of the scanner, computer, server, or other device estimatesfrom the measurements. A display device, network, or memory is used tooutput the estimated tissue property.

Additional, different, or fewer acts may be provided. For example, acts33 and/or 38 are not provided. As another example, acts 36 and 37 arealternatives or may be used together, such as averaging results fromboth. In another example, acts for configuring the ultrasound scannerand/or scanning are provided.

The acts are performed in the order described or shown (e.g., top tobottom or numerically), but may be performed in other orders. Forexample, acts 30, 32, and 33 are performed simultaneously, such as usingthe same transmit and receive pulses, or are performed in any order.

In act 30, an ultrasound scanner generates a measure of scattering intissue from a scan of a patient. The measure of scatter measures atissue response to a longitudinal wave transmitted from an ultrasoundscanner. The scattering or echo of the longitudinal wave impinging onthe tissue is measured.

Any measure of scatter may be used. Example scatter parameters includesound speed, sound dispersion, angular scattering coefficient (e.g.,backscatter coefficient), frequency-dependent attenuation coefficient,attenuation coefficient slope, spectral slope of the normalizedlog-spectrum, spectral intercept of the normalized log-spectrum,spectral midband of the normalized log-spectrum, effective scattererdiameter, acoustic concentration, scatterer number density, meanscatterer spacing, nonlinearity parameter (B/A), and/or ratio ofcoherent to incoherent scattering.

More than one measure may be performed. For example, the ultrasoundsystem determines values for two or more scattering parameters of thepatient tissue. In one embodiment, the acoustic attenuation coefficient,backscatter coefficient, and/or spectral slope of a logarithm of thefrequency-dependent backscatter coefficient are measured.

To measure the scatter, the ultrasound scanner scans the tissue withultrasound. A sequence of transmit and receive events is performed toacquire the signals to estimate the quantitative ultrasound scatterparameters. In one embodiment, a one, two, or three-dimensional regionis scanned by a B-mode sequence (e.g., transmit a broadband (e.g., 1-2cycle) transmit beam and form one or more responsive receive beams). Anyscan format may be used, such as linear, sector, or vector. The transmitand receive operations may be repeated for each scan line. Narrow bandpulses (e.g., 3 or more cycles) may be transmitted and received atdistinct center frequencies with or without overlapping spectra. Narrowband transmit pulses may be used in a single or in multiple transmit andreceive events. The transmit pulses and corresponding receive beams maybe formed at different steering angles, such as sampling a same locationof tissue from different directions. Different steering may be performedjust for transmit or just for receive. Different transmit beams may havedifferent transmit powers and/or F numbers. The single transmit ormultiple transmits may be focused, unfocused, or use a plane wave. Anyscan sequence may be used.

Repetition with or without different transmit and/or receive settingsmay be used to measure the scatter once or to measure the scatterdifferently. Where multiple measures of the same scatter parameter areprovided for a same location, the measures may be averaged or combined.Measures from different locations, such as adjacent locations orlocations within a given range, may be averaged. For example, themeasure of scatter is a frequency dependent measure averaged frommultiple transmissions to a same location. Changes in the power spectraas a function of depth, angle, and/or frequency may be measured. Asanother example, estimates of the attenuation coefficient from differenttransmit and/or receive angles are averaged to reduce variance or usedto quantify the angular dependence of attenuation.

In one embodiment, the scanning to measure is adaptive. Thetransmissions and/or receptions may be adaptive. For example, results ofone measure are used to set the amplitude, angle, frequency, and/or F#for subsequent transmissions.

In one example, the attenuation coefficient is measured. Thereference-phantom method is used, but other measures of the attenuationcoefficient may be used. Acoustic energy has an exponential decay as afunction of depth. A measure of acoustic intensity as a function ofdepth before or without depth gain correction is performed. To removesystem effects, the measurement is calibrated based on measures ofacoustic intensity as a function of depth in a phantom. The measurementmay be subject to less noise by averaging over a one, two, orthree-dimensional region. The beamformed samples or acoustic intensitymay be converted to the frequency domain, and the calculation performedin the frequency domain.

In another example, the backscatter coefficient is measured. Theacoustic attenuation is determined. This acoustic attenuation is used todetermine a reference calibration. By calibrating for the acousticattenuation, the scattered energy is provided as the backscattercoefficient. The calculation may be performed in the frequency domain,providing measures as a function of frequency.

The spectral slope of the logarithm of the frequency-dependentbackscatter is measured from the backscatter coefficient. The log of thebackscatter coefficient is determined as a function of the frequency. Aline is fit (e.g., least squares) to the log of the backscatter as afunction of frequency to determine the spectral slope.

In act 32, the ultrasound scanner generates a measure of shear wavepropagation in the tissue from the scan of the patient. For shear waveimaging, an acoustic radiation force impulse (ARFI or pushing pulse) istransmitted to tissue. The impulse causes displacement of the tissue ata location, resulting in generation of a shear wave. The shear wavetravels generally transversely to the transmit beam of the pushingpulse. By tracking tissue displacement at one or more laterally spacedlocations, the shear wave passing those locations may be detected. Thetime for the shear wave to travel from the origin to the later locationand the distance between the locations provides a shear wave speed.

Any shear wave parameter may be determined. For example, a shear wavespeed or velocity in tissue is measured. Other shear wave parametersinclude angular and/or frequency-dependent shear wave speed, angular andfrequency-dependent shear wave attenuation, angular and/orfrequency-dependent storage modulus, angular and/or frequency-dependentloss modulus, viscosity, and/or angular and/or frequency-dependentacoustic absorption coefficient.

The acoustic absorption coefficient is from absorption of the acousticpulse, not from absorption of the shear wave. The acoustic absorption isdetermined as F ∝ αI/c, where F is the radiation force, I is theintensity of ARFI push pulse, c is the acoustic sound speed, and α isthe acoustic absorption coefficient.

To measure shear wave, a pushing pulse or ARFI is transmitted to a focallocation in the tissue. A reference scan for a resting state tissueposition is performed before the pushing pulse or after the tissuereturns to a resting state. The change in position or displacement oftissue at one or more locations space from the focal location aremeasured over time. Tracking scans are repetitively performed. Usingcorrelation or other measure of similarity, the axial, 2D, or 3D shiftof tissue from a reference time compared to a current tracking time isdetermined. The time of the maximum displacement indicates the time ofthe shear wave. Other timing may be used, such as the beginning or endof displacement. The time for the shear wave to reach the trackinglocation and the distance from the tracking location to the focallocation of the pushing pulse provides the shear wave velocity. Otherapproaches may be used, such as solving for shear wave velocity atmultiple locations by determining a shift in displacement profile(displacement as a function of time) for different tracking locations orsolving from displacements as a function of location.

The measurement of the shear wave parameters may be a function offrequency and/or angle. By transmitting pushing pulses in beams fromdifferent angles and/or at different frequencies, the measurement isrepeated. Spatio-temporal displacement profiles are used in the time orfrequency domain to determine the measure. The results from thedifferent angles may be used to determine an angular dependent measure.

The shear wave parameter may be measured at different locations. Themeasurement may be based on tissue displacement to one or a singlepushing pulse. The measurement may instead be based on tissuedisplacement to multiple pushing pulses. The measurement is repeated fordifferent regions using different pushing pulses.

To measure the shear wave parameter, both pushing pulse and trackingtransmissions occur. The displacements are measured by receivingacoustic response to the tracking transmissions and not the pushingpulse transmissions. The same scan used for measuring the scatteringparameter may be used to measure the shear wave parameter. For example,the reference scan prior to transmitting the pushing pulse and used fortracking is used to measure the scatter. In other embodiments, the scanfor the shear wave parameter uses different transmissions and/orreceptions than for the scattering parameter. The scan for measurementsis divided into separate sequences of transmit and receive events forthe different measurements.

The pushing pulse has a relatively long duration as compared to thetracking pulses, such as tens, hundreds or thousands of cycles for thepushing pulse and one-to-three cycles for the tracking transmissions.Where repetition is provided, different focal locations, frequencies,angles, powers, and/or F numbers may be used for the pushing pulses.

The same measurement may be repeated for a same location and/ordifferent locations. Different frequency, F number, angle, power, focallocations, and/or other differences may be used for any repetition. Theresulting measures may be used together to determine another measure ormay be combined, such as averaged to reduce noise.

The ultrasound scanner may adapt the scanning for the shear waveparameter measurement. For example, for an estimate of the attenuationcoefficient of the shear wave, the push pulse adapts. The centerfrequency, duration, f-number, or other characteristic of the push pulseis changed for a later transmission. The focus is tighter or weaker. Thedisplacement to create the shear wave is larger or lesser. As anotherexample, for an estimate of the absorption coefficient with an ARFI pushpulse, another push pulse is transmitted with a tighter focus or longerduration. The change may improve signal-to-noise ratio (SNR) and/orreduce variability in the measurements.

The adaptation is based on any information. For example, thedisplacement profile is compared to a reference or calibration profile.As another example, an amount of displacement of a maximum, mean, ormedian displacement is determined. The information may indicate a needfor a stronger or higher intensity pushing pulse or may indicate thatlesser intensity pushing pulses is needed, allowing for a shorter cooldown time.

In act 33, the ultrasound scanner generates an ARFI measure of axialdisplacement of the tissue. An ARFI transmission causes tissue todisplace along an axis or scan line of the transmit beam. Rather thantracking a shear wave, the tissue displacement along the axis caused bythe ARFI or in response to a longitudinal wave generated by the ARFI istracked over time.

Any ARFI measure may be used. For example, the attenuation of thelongitudinal wave of the ARFI pulse may be estimated from displacementstracked at locations spaced from the focal point of the ARFI. Themeasures may be at the focal point or other locations along the axialscan line.

To measure, the ARFI is transmitted along a scan line. Tracking scansare performed after transmission of the ARFI. The acoustic echoes fromthe tracking transmissions along the scan line are received. Thereceived data is correlated with a reference from prior to or afterARFI-caused displacements. The amount of displacement as a function oftime, location, transmit angle, and/or transmit frequency is determined.The amount of maximum displacement, displacement as a function of depth,and/or displacement as a function of time is used to calculate the ARFImeasure.

The same measurement may be performed at other times and/or locations.The results from the repetition may be used to derive yet anothermeasure or may be averaged.

The transmissions may adapt, such as adapting an F number, frequency,duration, power, and/or angle. The adaption may be in response to anymeasure, such as a magnitude of a maximum displacement.

Other measures may be used. Response of tissue to different types ofwaves and/or scanning are measured. One or more measures of a same typeare used. For a given measure, a single instance, average, ordistribution (e.g., standard deviation over time, duration, frequency,angle, and/or space) are performed. Any number of the same or differenttypes of measures may be performed.

In act 34, the ultrasound scanner or other image processor estimates thetissue property of the tissue of the patient from different measures.The measures from two or more different wave phenomena are used. Thevalues of two or more measures are used to estimate the tissue property.For example, both a measure of scattering and a measure of shear wavepropagation are used to estimate the tissue property. In anotherexample, a measure of on-axis displacement (e.g., ARFI measure) is usedwith the measure of acoustic scattering and/or measure of shear wavepropagation.

Other information may be used for estimating the tissue property. Forexample, clinical information for the patient is used. The clinicalinformation may be the medical history, age, body-mass index, sex,fasting or not, blood pressure, diabetic or not, and/or a bloodbiomarker measure. Example blood biomarkers include alanineaminotransferase (ALT) level, aspartate aminotransferase (AST) level,and/or alkaline phosphatase (ALP) level. Any information about thepatient may be included.

Any tissue property may be estimated. For example, the fat fraction oftissue is estimated. The fat fraction of the liver, breast, or othertissue is diagnostically useful. The fat fraction in a liver of thepatient assists in diagnosis of NAFLD. Other diagnostically usefultissue properties include inflammation, density, fibrosis, and/ornephron characteristic (count and/or diameter). The tissue property isbinary, such as existing or not, or is an estimate along a scale (i.e.,level or magnitude of the tissue property). Only one tissue property isestimated in one embodiment. In other embodiments, two or more differenttissue properties are estimated from the same or different measures.

Acts 36 and 37 represent two different embodiments for estimating in act34. The different embodiments are alternatives. Other embodiments may beused. Both or multiple embodiments may be used, such as determining avalue for a tissue property in two ways and then averaging the resultsor selecting the result most likely to be accurate.

The value of the tissue property is estimated. In the embodiment of act36, a machine-learnt classifier estimates the tissue property. Themachine-trained classifier provides a nonlinear model. Any machinelearning and resulting machine-learnt classifier may be used. Forexample, a support vector machine, probabilistic boosting tree, Bayesiannetwork, neural network, or other machine learning is used.

The machine learning learns from training data. The training dataincludes various examples, such as tens, hundreds, or thousands ofsamples, and the ground truth. The examples include the input data to beused, such as values for scattering and shear wave propagationparameters. The ground truth is the value for the tissue property ofeach example. In one embodiment, the machine learning is to learn toclassify the fat fraction based on scattering and shear wave propagationparameters. The ground truth for the fat fraction is provided with amagnetic resonance (MR) scan providing proton density fat fraction(PDFF). The MR-PDFF provides a percentage of fat for a location orregion. The percentage of fat is used as the ground truth so that themachine learning learns to classify the percentage of fat from inputvalues for the ultrasound parameters. Other sources of ground truth maybe used for a given tissue property, such as from biopsies, modeling, orother measurements.

In one embodiment, the machine learning trains a neural network. Theneural network includes one or more convolution layers that learn afilter kernel to distinguish between the values of a tissue property.The machine training learns what weighted combination (e.g., convolutionusing learnt kernel) of input values indicates the output. The resultingmachine-learnt classifier uses the input values to extract thedistinguishing information and then classifies the tissue property basedon the extracted information.

The training provides one or more matrices. The matrix or matricesrelate the input information to the output class. Hierarchal trainingand a resulting classifier may be used. Different classifiers may beused for different tissue properties. Multiple classifiers may be usedfor a same tissue property and the results averaged or combined.

In the embodiment of act 37, a linear model is used instead of or inaddition to a machine-learnt model. A predetermined or programmedfunction relates the input values to the output values. The functionand/or weights used in the function may be determined experimentally.For example, the weights are obtained by a least square minimizationusing MR-PDFF values.

Any linear function may be used. For example, the value of the tissueproperty is estimated from one or more scatter parameters and one ormore shear wave propagation parameters. Any combination of addition,subtraction, multiplication, or division may be used.

In one embodiment, two or more functions (e.g., weighted combinations ofmeasures) are provided. One of the functions is selected based on thevalue of one of the parameters. For example, the ultrasound-derived fatfraction (UDFF) estimation includes two functions, represented asweighted combinations:

UDFF=aP ₁ +bP ₂ +cP ₃ + . . . d for P _(i:min) <P _(i) <P _(i:max)

UDFF=αP ₁ +βP ₂ +cγ+ . . . δ for P _(k:min) <P _(k) <P _(k:max)

where d and δ are constants, a, b, c, α, and β are weights, and P is ameasure of a parameter. One parameter P_(i,k) is used to determine whichfunction to select. The possible functions include two or three otherparameters and weights. Additional, different, or fewer numbers offunctions, parameters in a function, weights, and/or constants may beused. Different selection criteria may be used. The selection parametermay be of one type and the weighted parameters of each function ofanother type. Alternatively, different types (e.g., scattering and shearwave propagation) are included as weighted parameters regardless of thetype or types of parameters used for selection.

In one example, AC is the acoustic attenuation coefficient (e.g., ascattering parameter), BSC is the backscatter coefficient (e.g., ascattering parameter), and SS is the spectral slope of the logarithm ofthe frequency-dependent backscatter coefficient (e.g., also a scatteringparameter). SWS is the shear wave speed (e.g., a shear wave propagationparameter). Two functions based on scattering parameters are used, wherethe function for a given estimation is selected based on the shear wavepropagation parameter, as represented as:

${UDFF} = {{{55{AC}} + {114{BSC}} - {42\mspace{14mu} {for}\mspace{14mu} {SWS}}} < \frac{1.3\mspace{14mu} m}{s}}$UDFF = −3.8SS + 425BSC − 9.4  for  SWS > 1.3  m/s

The weights and constants are based on minimizing a difference from thefat fraction provided by MR-PDFF. Expert selected or other weightsand/or constants may be used.

In act 38, the ultrasound scanner or a display device displays theestimated tissue parameter. For example, an image of the fat fraction isgenerated. A value representing the estimated fat fraction is displayedon a screen. Alternatively or additionally, a graphic (e.g., curve oricon) representing the estimated fat fraction is displayed. Reference toa scale or other reference may be displayed. In other embodiments, thefat fraction as a function of location is displayed by color,brightness, hue, luminance, or other modulation of display values in aone, two, or three-dimensional representation. The tissue property maybe mapped linearly or non-linearly to pixel color.

The tissue property is indicated alone or with other information. Forexample, shear wave imaging is performed. The shear wave velocity,modulus or other information determined from tissue reaction to a shearwave is displayed. Any shear imaging may be used. The displayed imagerepresents shear wave information for the region of interest or theentire imaging region. For example, where shear velocity values aredetermined for all the grid points in a region of interest or field ofview, the pixels of the display represent the shear wave velocities forthat region. The display grid may be different from the scan grid and/orgrid for which displacements are calculated.

The shear wave information is used for a color overlay or othermodulation of display values. Color, brightness, luminance, hue, orother display characteristic is modulated as a function of the shearwave characteristic, such as the shear wave velocity. The imagerepresents a two- or three-dimensional region of locations. The sheardata is in a display format or may be scan converted into a displayformat. The shear data is color or gray scale data but may be data priorto mapping with gray scale or color scale. The information may be mappedlinearly or non-linearly to the display values.

The image may include other data. For example, shear wave information isdisplayed over or with B-mode information. B-mode or other datarepresenting tissue, fluid, or contrast agents in the same region may beincluded, such as displaying B-mode data for any locations with shearwave velocity below a threshold or with poor quality. The other dataassists the user in determining the location of the shear information.In other embodiments, the shear wave characteristic is displayed as animage without other data. In yet other embodiments, the B-mode or otherimage information is provided without shear wave information.

The additional estimated value of the tissue property is displayedsubstantially simultaneously with the shear wave, B-mode, color or flowmode, M-mode, contrast agent mode, and/or other imaging. Substantiallyaccounts for visual perception of the view. Displaying two imagessequentially with sufficient frequency may allow the viewer to perceivethe images as being displayed at a same time. The component measuresused to estimate the tissue property may also be displayed, such as in atable.

Any format for substantially simultaneous display may be used. In oneexample, the shear wave or anatomy image is a two-dimensional image. Thevalue of the tissue property is text, a graph, two-dimensional image, orother indicator of the values of the estimate. A cursor or otherlocation selection may be positioned relative to the shear or anatomyimage. The cursor indicates selection of a location. For example, theuser selects a pixel associated with an interior region of a lesion,cyst, inclusion, or other structure. The tissue property for theselected location is then displayed as a value, a pointer along a scale,or other indication. In another example, the tissue property isindicated in a region of interest (sub-part of the field of view) orover the entire field of view.

In another embodiment, shear wave or B-mode and fat fraction images aredisplayed substantially simultaneously. For example, a dual-screendisplay is used. The shear wave image (e.g., shear wave velocity) and/orB-mode image are displayed in one area of the screen. The fat fractionas a function of location is displayed in another area of the screen.The user may view the different images on the screen for diagnosis. Theadditional information or indication of the tissue property helps theuser diagnose the region.

In one embodiment, the tissue estimation is provided as a real-timenumber or quantitative image. Since the tissue parameters may beestimated quickly, the value of the tissue parameter is estimated andoutput within 1-3 seconds of starting the scanning. The tissue propertymay be estimated at different times, such as before, during and/or aftertreatment. The estimates from the different times are used to monitorprogression of the disease and/or response to therapy. For example, apercent change in the value of the tissue property over time iscalculated and output.

The tissue property and/or measurements used to derive tissue propertiesmay be used in estimation of disease activity. FIG. 3 is a flow chartdiagram of one embodiment of a method for disease activity estimationwith an ultrasound system. For example, the method is forultrasound-derived non-alcoholic liver disease activity estimation. Anultrasound scanner measures scatter and/or shear wave propagation intissue of a patient for directly or indirectly estimating the diseaseactivity.

For example, non-alcoholic fatty liver disease activity score (NAS) ispredicted using quantitative ultrasound. NAS is predicted based onultrasound estimates of tissue mechanical and acoustic properties. Amodel predicts NAS based on tissue mechanical and acoustic propertiesestimated using medical ultrasound. In one embodiment, an ultrasoundsystem noninvasively obtains an ultrasound-derived NAFLD activity (UDNA)index as a predictor of NAS. The ultrasound system is configured toperform a pulse sequence for generating measures of scattering and shearwave propagation. The UDNA is determined using a model of at least threeproperties of the liver including the acoustic backscatter coefficient,the shear wave velocity, and the shear wave damping ratio.

Histological NAS is the sum of steatosis, lobular inflammation, andballooning histologic scores but requires biopsy. In one embodiment, theproposed ultrasound-derived model pairs appropriate mechanical andacoustic properties with NAS features. The ultrasound-derived fatfraction, based on backscatter, attenuation, and/or sound speed, is usedas a measure of steatosis grade. Shear wave damping ratio is used as ameasure of inflammation, and shear wave speed is used as a measure ofballooning. Other ultrasound measurements may be used.

The method of FIG. 3 is implemented by the system of FIG. 2 or adifferent system. A medical diagnostic ultrasound scanner performs themeasurements by acoustically generating the waves and measuring theresponses. An image processor of the scanner, computer, server, or otherdevice estimates from the measurements. A display device, network, ormemory is used to output the estimated disease activity score.

Additional, different, or fewer acts may be provided. For example, acts33 from FIG. 1 is included, such as using the ARFI measurement in theestimation of fat fraction or other tissue property. As another example,act 34 is not included, such as where the disease activity index scoreis estimated from the measurements without a separate estimation of thetissue property (e.g., fat fraction). In yet another example, act 38 isnot provided. In another example, acts for configuring the ultrasoundscanner and/or scanning are provided.

The acts are performed in the order described or shown (e.g., top tobottom or numerically), but may be performed in other orders. Forexample, acts 30 and 32 are performed simultaneously, such as using thesame transmit and receive pulses, or are performed in any order.

In act 30, the ultrasound scanner generates one or more measures ofscattering in tissue from an ultrasound scan of a patient. Any acousticscatter parameters may be used, such as measurements of acousticinteraction with liver tissue. For example, the ultrasound scanner orsystem measures the backscatter coefficient, frequency-dependentbackscatter coefficient, attenuation, sound speed, and/or any other ofthe scatter measurements discussed above for FIG. 1. The measure may befrequency dependent, such as averaged from multiple transmissions.Adaptive scanning may be used.

The measures of scattering may be used for estimating fat fraction, suchas using acoustic backscatter (e.g., frequency dependent acousticbackscatter) and acoustic attenuation. The measures of scattering arealternatively or additionally used in estimation of the diseaseactivity, such as using acoustic backscatter or frequency dependentacoustic backscatter.

In act 32, the ultrasound scanner generates one or more measures ofshear wave propagation in the tissue from the ultrasound scan of thepatient. Any shear wave propagation parameters may be used. For example,the shear wave velocity and shear wave damping ratio are used. Any ofthe shear wave propagation measurements discussed above for FIG. 1 maybe used. The measurements are for an ARFI induced shear wave in thetissue of interest, such as the liver tissue, of the patient. Adaptivescanning may be used.

As discussed above for FIG. 1, the ultrasound scanning used formeasuring scattering and for measuring shear wave propagation use thesame or different transmit and receive events. For example, separatetransmissions and receptions are used for measuring scattering than usedto generate the shear wave and measure the tissue response to the shearwave.

In one embodiment, a shear wave damping ratio is generated. Theultrasound scanning is performed to measure tissue response to the shearwave in order to determine shear wave viscosity as a complex number,such as a ratio of the storage modulus to the loss modulus. This complexrepresentation uses the real part of the viscosity as the storagemodulus and the imaginary part of the viscosity as the loss modulus.

In one approach, spatio-temporal displacement measurements are acquiredduring shear wave propagation. These measurements are Fouriertransformed into the frequency domain, such as using a fast Fouriertransform, and used to determine the complex wave number. The log of aspectrum of displacement as a function of time may be determined foreach of various locations subjected to a shear or other wave. Solvingusing the log as a function of location provides the complex wavenumber.Various viscoelastic parameters, such as loss modulus and storagemodulus, are determined from the complex wavenumber. In one embodiment,the measurements to determine complex wavenumber, viscosity, or otherdamping ratio measurement disclosed in U.S. Published Patent ApplicationNo. 2016/0302769 are used.

As another approach, the shear wave attenuation and shear wavedispersion are measured. The dispersion is a change in shear wave speedor velocity as a function of frequency. The shear wave attenuation mayalso be measured as a function of frequency. For a given frequency orfor a combination (e.g., average) from multiple frequencies, a phasor isgenerated based on the attenuation and dispersion values. This phasor isconverted to a complex number, from which the real and imaginary partsare used as the damping ratio. Other measures of the damping ratio fromtissue response to shear wave may be used.

The image processor estimates a value for an ultrasound-derived liverdisease activity index from the backscatter coefficient, the shear wavevelocity, and the shear wave damping ratio. Other measurements may beused. Non-ultrasound information may additionally be used, such as fromthe patient medical record.

The disease activity is estimated directly or indirectly from themeasurements. For direct measurements, the measurements are input to amodel, which outputs the estimate of the value of the disease activityin act 40. The score is directly estimated from the measurements. Forindirect measurements, one or more types of measurements are used todetermine another value or estimate (e.g., fat fraction) in act 34,which estimate is then used alone, with other types of estimates, withother measurements, or with other types of estimates and othermeasurements to estimate the disease activity in act 40.

In one embodiment, the backscatter coefficient, acoustic attenuation,and/or shear wave velocity (e.g., using backscatter coefficient(acoustic scattering) and acoustic attenuation without shear wavevelocity) are used to estimate the fat fraction in act 34. One or morescattering and/or one or more shear wave propagation parameters are usedto estimate fat fraction. Any of the embodiments for estimating fatfraction discussed above for FIG. 1 may be used. For example, theacoustic attenuation coefficient (e.g., a scattering parameter), thebackscatter coefficient (e.g., a scattering parameter), and the spectralslope of the logarithm of the frequency-dependent backscattercoefficient (e.g., also a scattering parameter) are used to estimate fatfraction. The shear wave speed (e.g., a shear wave propagationparameter) may be used, such as to select the function for estimatingthe fat fraction from the scattering parameters.

In act 40, the image processor estimates a level of the liver diseaseactivity (e.g., NAS or UDNA). For indirect estimate, the level ofdisease activity is estimated from the fat fraction and at least one ofthe shear wave parameters. For example, three inputs are used in theestimate of disease activity. Ultrasound mechanical and acousticproperties replace histological NAS features. The ultrasound-derived fatfraction, such as based on backscatter, attenuation, and/or sound speed,is used as a measure of steatosis grade. Shear wave damping ratio isused as a measure of inflammation, and shear wave speed is used as ameasure of ballooning. The value of the liver disease activity for anindex to assist a physician is estimated directly or indirectly fromvarious measurements and/or estimates, such as the fat fraction, thedamping ratio, and the shear wave velocity.

Other measurements and/or estimates may be used as replacements.Multiple estimates and/or measurements may be used in place on onehistological NAS variable. The activity may be estimated from estimatesand/or measurements different than the histological variables, providinga different approach to determining disease activity.

The image processor estimates the score or value for the liver diseaseactivity index using a model. The score or value is part of an index,such as over a range of integers. Any range, such as three levels (e.g.,steatohepatitis, cirrhosis, or hepatocellular carcinoma), two levels(e.g., steatosis or fibrosis), or four or more levels, may be used. Thescoring may relate to particular stages of disease or may indicate levelof disease without reference to particular stages (e.g., 0-7 providing 8levels with different amounts of activity for each level). The estimatedvalue or score is of a stage and/or numerical representation.

The model may be a function, such as using the estimates and/ormeasurements in variables. In other embodiments, the model is amachine-learnt classifier. Machine learning, such as using a fullyconnected neural network, a convolutional neural network, or a supportvector machine, trains the model to classify—output the value of thedisease activity given the input estimates and/or measurements. Inanother embodiment, a logistic regression model is used. The value ofthe disease activity is estimated from the measurements and/or estimateswith logistic regression. For example, the disease activity is estimatedwith a logistic regression of the fat fraction, the shear wave velocity,and the damping ratio. As another example, the disease activity isestimated as a logistic regression of the backscatter coefficient, theshear wave velocity, and the shear wave damping ratio.

The image processor may use other input information to estimate thedisease activity. For example, measurements of tissue response to alongitudinal wave from ARFI may be used. As another example, clinicalinformation for the patient may be used.

In act 42, the image processor generates and a display (e.g., displayscreen) displays the estimate of the disease activity. The level ofliver disease activity (e.g., UDNA) is displayed to the user to assistin disease diagnosis or monitoring. Since ultrasound is used, theassistance is provided without invasive biopsy and/or time and expenseof MRI.

The level (e.g., value) of the UNDA index or other scale of diseaseactivity is output as alphanumeric text, as a graph, or as color codingor labeling in an image representing tissue of the patient. For example,an ultrasound image of the liver tissue is displayed. The image includesan indication of the value of the ultrasound-derived liver diseaseactivity index as estimated. The fat fraction, other tissue propertyestimate, and/or measurements may also be output. Any of the outputsdiscussed above for act 38 may be used with the disease activity insteadof or in addition to the fat fraction.

FIG. 4 shows a graph of predicted NAS compared to histologic NAS. Thepredicted NAS is an estimation of UDNA from logistic regression from fatfraction, shear wave velocity, and shear wave damping ratio based onshear wave propagation. The index uses eight levels for scoring (0-7).Based on 82 patients, the root mean square error between predicted NASand histologic NAS is 1.14. The predicted correlates well with thehistologic NAS, particularly for the mid-range scores of 2-5. Gooddiagnostic performance is provided.

FIG. 2 shows one embodiment of a system 10 for tissue property and/ordisease activity estimation from measures responsive to different typesof waves. The system 10 implements the method of FIG. 1, the method ofFIG. 3, or other methods. The system 10 includes a transmit beamformer12, a transducer 14, a receive beamformer 16, an image processor 18, adisplay 20, and a memory 22. Additional, different or fewer componentsmay be provided. For example, a user input is provided for userinteraction with the system.

The system 10 is a medical diagnostic ultrasound imaging system. Inalternative embodiments, the system 10 is a personal computer,workstation, PACS station, or other arrangement at a same location ordistributed over a network for real-time or post acquisition imaging.

The transmit and receive beamformers 12, 16 form a beamformer used totransmit and receive using the transducer 14. Sequences of pulses aretransmitted, and responses received based on operation or configurationof the beamformer. The beamformer scans for measuring scatter, shearwave, and/or ARFI parameters. The beamformers 12, 16 are configured totransmit and receive sequences of pulses in a patient with thetransducer 14. The sequence of pulses is for one or more scatterparameters and for one or more shear wave parameters.

The transmit beamformer 12 is an ultrasound transmitter, memory, pulser,analog circuit, digital circuit, or combinations thereof. The transmitbeamformer 12 is operable to generate waveforms for a plurality ofchannels with different or relative amplitudes, delays, and/or phasing.Upon transmission of acoustic waves from the transducer 14 in responseto the generated electrical waveforms, one or more beams are formed. Asequence of transmit beams are generated to scan a two orthree-dimensional region. Sector, Vector®, linear, or other scan formatsmay be used. The same region may be scanned multiple times usingdifferent scan line angles, F numbers, and/or waveform centerfrequencies. For flow or Doppler imaging and for shear imaging, asequence of scans along the same line or lines is used. In Dopplerimaging, the sequence may include multiple beams along a same scan linebefore scanning an adjacent scan line. For shear imaging, scan or frameinterleaving may be used (i.e., scan the entire region before scanningagain). Line or group of line interleaving may be used. In alternativeembodiments, the transmit beamformer 12 generates a plane wave ordiverging wave for more rapid scanning.

The same transmit beamformer 12 generates impulse excitations orelectrical waveforms for generating acoustic energy to causedisplacement. Electrical waveforms for acoustic radiation force impulsesare generated. In alternative embodiments, a different transmitbeamformer is provided for generating the impulse excitation. Thetransmit beamformer 12 causes the transducer 14 to generate pushingpulses or acoustic radiation force pulses.

The transducer 14 is an array for generating acoustic energy fromelectrical waveforms. For an array, relative delays focus the acousticenergy. A given transmit event corresponds to transmission of acousticenergy by different elements at a substantially same time given thedelays. The transmit event may provide a pulse of ultrasound energy fordisplacing the tissue. The pulse may be an impulse excitation, trackingpulse, B-mode pulse, or pulse for other measures. Impulse excitationincludes waveforms with many cycles (e.g., 500 cycles) but that occursin a relatively short time to cause tissue displacement over a longertime. A tracking pulse may be B-mode transmission, such as using 1-5cycles. The tracking pulses are used to scan a region of a patient.

The transducer 14 is a 1-, 1.25-, 1.5-, 1.75- or 2-dimensional array ofpiezoelectric or capacitive membrane elements. The transducer 14includes a plurality of elements for transducing between acoustic andelectrical energies. Receive signals are generated in response toultrasound energy (echoes) impinging on the elements of the transducer14. The elements connect with channels of the transmit and receivebeamformers 12, 16. Alternatively, a single element with a mechanicalfocus is used.

The receive beamformer 16 includes a plurality of channels withamplifiers, delays, and/or phase rotators, and one or more summers. Eachchannel connects with one or more transducer elements. The receivebeamformer 16 is configured by hardware or software to apply relativedelays, phases, and/or apodization to form one or more receive beams inresponse to each imaging or tracking transmission. Receive operation maynot occur for echoes from the impulse excitation used to displacetissue. The receive beamformer 16 outputs data representing spatiallocations using the receive signals. Relative delays and/or phasing andsummation of signals from different elements provide beamformation. Inalternative embodiments, the receive beamformer 16 is a processor forgenerating samples using Fourier or other transforms.

The receive beamformer 16 may include a filter, such as a filter forisolating information at a second harmonic or other frequency bandrelative to the transmit frequency band. Such information may morelikely include desired tissue, contrast agent, and/or flow information.In another embodiment, the receive beamformer 16 includes a memory orbuffer and a filter or adder. Two or more receive beams are combined toisolate information at a desired frequency band, such as a secondharmonic, cubic fundamental or another band.

In coordination with the transmit beamformer 12, the receive beamformer16 generates data representing the region. For tracking a shear wave oraxial longitudinal wave, data representing the region at different timesis generated. After the acoustic impulse excitation, the receivebeamformer 16 generates beams representing locations along one or aplurality of lines at different times. By scanning the region ofinterest with ultrasound, data (e.g., beamformed samples) is generated.By repeating the scanning, ultrasound data representing the region atdifferent times after the impulse excitation is acquired.

The receive beamformer 16 outputs beam summed data representing spatiallocations. Data for a single location, locations along a line, locationsfor an area, or locations for a volume are output. Dynamic focusing maybe provided. The data may be for different purposes. For example,different parts of a scan are performed for B-mode or tissue data thanfor displacement. Alternatively, the B-mode data is also used todetermine displacement. As another example, data for different types ofmeasures are acquired with a series of shared scans, and B-mode orDoppler scanning is performed separately or using some of the same data.

The image processor 18 is a B-mode detector, Doppler detector, pulsedwave Doppler detector, correlation processor, Fourier transformprocessor, application specific integrated circuit, general processor,control processor, image processor, field programmable gate array,digital signal processor, analog circuit, digital circuit, combinationsthereof or other now known or later developed device for detecting andprocessing information for display from beamformed ultrasound samples.In one embodiment, the image processor 18 includes one or more detectorsand a separate image processor. The separate image processor is acontrol processor, general processor, digital signal processor,application specific integrated circuit, field programmable gate array,network, server, group of processors, data path, combinations thereof orother now known or later developed device for calculating values ofdifferent types of parameters from beamformed and/or detected ultrasounddata and/or for estimating from the values from the different types ofmeasures. For example, the separate image processor is configured byhardware, firmware, and/or software to perform acts 34-38 shown in FIG.1 and/or acts 34-42 shown in FIG. 3.

The image processor 18 is configured to estimate a value for the tissueproperty and/or disease activity from a combination of different typesof parameters. For example, a measured scatter parameter and one, two,or more measured shear wave parameters are used. The different types ofparameters are measured based on the transmit and receive sequences andcalculation from the results. The values of the one or more measures ofeach of at least two of the types (e.g., scatter, shear wavepropagation, or axial ARFI) of parameters are determined for fatfraction estimation. The values of one or more scatter and one or more(e.g., two) shear wave propagation parameters are determined for liverdisease activity estimation.

In one embodiment, the image processor 18 estimates the tissue propertybased on the different types of parameters or measures of tissuereaction to different types of wave fronts. The estimation applies amachine-learnt classifier. The input values of the measures with orwithout other information are used by a learnt matrix to output a valueof the tissue property. In other embodiments, the image processor 18uses a weighted combination of the values of the parameters. Forexample, two or more functions are provided. Using the value of one ormore parameters (e.g., shear wave speed), one of the functions isselected. The selected function uses the values of the same and/ordifferent parameters to determine the value of the tissue property. Alinear or non-linear mapping relates values of one or more parameters tothe value of the tissue property. For example, two or more scatterparameters are used to determine the value of the tissue property with ashear wave propagation selected function.

In another embodiment, the image processor 18 is configured to generatea score for an index of the disease activity from a combination of oneor more scatter parameters and one or more (e.g., two) shear waveparameters. For example, the image processor 18 estimates a fat fractionof a liver of the patient from one or more scatter parameters. The imageprocessor 18 generates the score as an ultrasound-derived non-alcoholicliver disease activity from the fat fraction and two or more shear waveparameters. The score is generated with a machine-learnt classifier or alogistic regression model. For example, the logistic regression modelrelates the scatter (e.g., acoustic backscatter coefficient) and two ormore shear wave parameters (e.g., shear wave velocity and shear wavedamping ratio) to the level of disease activity.

The processor 18 is configured to generate one or more images. Forexample, a shear wave velocity, B-mode, contrast agent, M-mode, flow orcolor mode, ARFI, and/or another type of image is generated. The shearwave velocity, flow, or ARFI image may be presented alone or as anoverlay or region of interest within a B-mode image. The shear wavevelocity, flow, or ARFI data modulates the color at locations in theregion of interest. Where the shear wave velocity, flow, or ARFI data isbelow a threshold, B-mode information may be displayed withoutmodulation by the shear wave velocity.

Other information is included in the image or displayed sequentially orsubstantially simultaneously. For example, a tissue property estimateimage and/or disease activity level are displayed at a same time as theother image. A value or values of the tissue property and/or diseaseactivity map may display information. Where the tissue property and/ordisease activity are measured at different locations, the values of thetissue property and/or disease activity may be generated as a coloroverlay in the region of interest in B-mode images. The shear wavevelocity, tissue property, and/or disease activity data may be combinedas a single overlay on one B-mode image. Alternatively, the value orvalues of the tissue property and/or disease activity are displayed astext or numerical value(s) adjacent or overlaid on a B-mode or shearwave imaging image. The image processor 18 may be configured to generateother displays. For example, the shear wave velocity image is displayednext to a graph, text, or graphical indicators of the tissue property,such as fat fraction and/or degree of fibrosis, and/or disease activity,such as an index value indicting level of UDNA. The tissue propertyinformation and/or disease activity are presented for one or morelocations of the region of interest without being in a separate two orthree-dimensional representation, such as where the user selects alocation and the ultrasound scanner then presents the tissue propertyand/or disease activity for that location.

The image processor 18 operates pursuant to instructions stored in thememory 22 or another memory for estimation from measures of tissuereaction to different types of waves (e.g., scattering from atransmitted ultrasound, on-axis tissue displacement, and/or a shear wavecaused by tissue displacement). The memory 22 is a non-transitorycomputer readable storage media. The instructions for implementing theprocesses, methods and/or techniques discussed herein are provided onthe computer-readable storage media or memories, such as a cache,buffer, RAM, removable media, hard drive or other computer readablestorage media. Computer readable storage media include various types ofvolatile and nonvolatile storage media. The functions, acts or tasksillustrated in the figures or described herein are executed in responseto one or more sets of instructions stored in or on computer readablestorage media. The functions, acts or tasks are independent of theparticular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firmware, micro code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing, and the like. In oneembodiment, the instructions are stored on a removable media device forreading by local or remote systems. In other embodiments, theinstructions are stored in a remote location for transfer through acomputer network or over telephone lines. In yet other embodiments, theinstructions are stored within a given computer, CPU, GPU or system.

The display 20 is a device, such as a CRT, LCD, projector, plasma, orother display for displaying one or two-dimensional images orthree-dimensional representations. The two-dimensional images representspatial distribution in an area. The three-dimensional representationsare rendered from data representing spatial distribution in a volume.The display 20 is configured by the image processor 18 or other deviceby input of the signals to be displayed as an image. The display 20displays an image representing the tissue property and/or diseaseactivity for a single location (e.g., averaged from tissue propertyestimates including adjacent locations), in a region of interest, or anentire image. For example, the display 20 displays a value for fatfraction and/or a score for an index of disease activity. The display ofthe tissue property and/or disease activity based on the different typesof waves provides more accurate tissue property or level of diseaseinformation for diagnosis.

While the invention has been described above by reference to variousembodiments, it should be understood that many changes and modificationscan be made without departing from the scope of the invention. It istherefore intended that the foregoing detailed description be regardedas illustrative rather than limiting, and that it be understood that itis the following claims, including all equivalents, that are intended todefine the spirit and scope of this invention.

I (We) claim:
 1. A method for non-alcoholic liver disease activityestimation with an ultrasound scanner, the method comprising: generatinga first measure of scattering in tissue from a scan of a patient by theultrasound scanner, the first measure of scattering comprising abackscatter coefficient; generating second and third measures of shearwave propagation in the tissue from the scan of the patient by theultrasound scanner, the second measure comprising a shear wave velocityand the third measure comprising a shear wave damping ratio; estimatinga first value for an ultrasound-derived liver disease activity indexfrom the backscatter coefficient, the shear wave velocity, and the shearwave damping ratio; and outputting an ultrasound image including anindication of the first value of the ultrasound-derived liver diseaseactivity index as estimated.
 2. The method of claim 1 wherein generatingthe first, second, and third measures from the scan comprises separatetransmit and receive events for (1) the first measure of scattering and(2) the second and third measures of shear wave propagation.
 3. Themethod of claim 1 wherein generating the first measure of scatteringcomprises generating a frequency-dependent backscatter coefficient asthe backscatter coefficient.
 4. The method of claim 1 wherein generatingthe third measure comprises generating the shear wave damping ratio as aratio of real and imaginary parts of a complex number from a Fouriertransformation of spatial-temporal displacements caused by the shearwave propagation.
 5. The method of claim 1 wherein generating the thirdmeasure comprises generating the shear wave damping ratio from a shearwave attenuation and a shear wave dispersion.
 6. The method of claim 1wherein estimating the first value comprises estimating a fat fractionof a liver of the patient from an acoustic attenuation, the backscattercoefficient, and the shear wave velocity, and estimating the first valuefor the ultrasound-derived liver disease activity index from the fatfraction, the damping ratio, and the shear wave velocity.
 7. The methodof claim 6 wherein estimating the first value comprises estimating witha logistic regression of the fat fraction, the shear wave velocity, andthe damping ratio.
 8. The method of claim 1 wherein estimating comprisesestimating with a machine-learnt classifier.
 9. The method of claim 1wherein estimating comprises estimating with a logistic regressionmodel.
 10. The method of claim 8 wherein estimating with the logisticregression model comprises estimating as a logistic regression of thebackscatter coefficient, the shear wave velocity, and the shear wavedamping ratio.
 11. The method of claim 1 wherein generating the measureof scatter comprises generating the measure of scatter as a frequencydependent measure averaged from multiple transmissions.
 12. The methodof claim 1 wherein generating the first, second, and third measurescomprises adaptive scanning.
 13. The method of claim 1 whereinestimating comprises estimating as a function of clinical informationfor the patient.
 14. A system for estimation of disease activity, thesystem comprising: a transducer; a beamformer configured to transmit andreceive sequences of pulses in a patient with the transducer, thesequence of pulses being for a scatter parameter and for first andsecond shear wave parameters; an image processor configured to generatea score for an index of the disease activity from a combination of thescatter parameter, the first shear wave parameter, and the second shearwave parameter; and a display configured to display the score for theindex of the disease activity.
 15. The system of claim 14 wherein theimage processor is configured to generate the score with amachine-learnt classifier.
 16. The system of claim 14 wherein the imageprocessor is configured to generate the score with a logistic regressionmodel of the scatter parameter, the first shear wave parameter, and thesecond shear wave parameter.
 17. The system of claim 14 wherein thescatter parameter comprises acoustic backscatter coefficient, the firstshear wave parameter comprises shear wave velocity, and the third shearwave parameter comprises shear wave damping ratio.
 18. The system ofclaim 14 wherein the image processor is configured to estimate a fatfraction of a liver of the patient from the scatter parameter, and theimage processor is configured to generate the score as anultrasound-derived non-alcoholic liver disease activity from the fatfraction, the first shear wave parameter, and the second shear waveparameter.
 19. A method for liver disease activity estimation with anultrasound system, the method comprising: determining, by the ultrasoundsystem, a plurality of scattering parameters of liver tissue of apatient; determining, by the ultrasound system, a plurality of shearwave parameters of the liver tissue of the patient; estimating a fatfraction from at least one of the scattering parameters; estimating alevel of the liver disease activity from the fat fraction and at leastone of the shear wave parameters; and displaying the level of the liverdisease activity.
 20. The method of claim 19 wherein estimating the fatfraction comprises estimating from an acoustic attenuation and anacoustic scattering as the scattering parameters and wherein estimatingthe level of liver disease activity comprises estimating from the fatfraction and from shear wave velocity and shear wave damping ration asthe shear wave parameters.